Machine learning Mindmap
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This mind map is curated by me and created using MindMeister.
![Machine Learning
Enterprise
Security/Fraud
BrightPoint Sentinel automate threat detection
and risk analysis
HR/Recruiting
Textio analyzed job text and outcomes data
using listings from tens of thousands of
companies
hiQ People Analytics helps employee selection,
development and retention by modeling
historical data to predict future outcomes
Sales
Sentient Aware uses visual search to help
shoppers quickly find the products they want to
buy just like a store associate, connecting the
right products to every customer
Marketing
LiftIgniter improves CTR,
engagement and conversion by
providing personalization using
recommendation in real-time
Customer Support
Clarabridge collects customer feedback from
various sources and provide actionable
insights
Quantifind tells what's most important in
driving people to buy your products by
introducing brand strategy. Explanatory
analytics
potential replacement for survey-based
consumer research, brand health studies, focus
groups, strategic consulting engagements, etc.
Internal Intel
Using the combination of machine learning and
crowdsourcing from experts identified by their
usage of tables, Alation centralizes the
knowledge on data and ensures it’s always
up-to-date
Market Intel
Mattermark mines and crunches public Internet
data to provide investors, sales teams and
others with search tools and other business
intelligence,
Purpose
Increase Sales Performance
1) Don’t worry if data isn’t 100% accurate to begin with. As long as it’s directionally correct it will stimulate the right discussions.
Data quality will improve naturally with use, feedback, updating, and iterative cleansing.
2) Drive excitement and adoption by making the application simple and engaging for the field, with easy-to-understand,
interactive visualizations.
3) Integrate predictive analytics into the visualization and discovery process on a self-service basis so that new insights are
intuitively delivered as the underlying data and attributes change. This will keep the insights from the application relevant.
4) Use iterative techniques to design and deliver a working app quickly and then adapt it based on user feedback.
5) Partner with IT through this process so that the users receive the desired self-service and flexibility while leveraging the business
intelligence platform to maintain data governance, security, and control
Increase number of customers
reducing attrition/churn using historical data
and look for likelihood of churn
acquiring new customers by lead scoring and
optimizing marketing campaigns
Serve customer better
cross-selling products
optimizing products and pricing by mapping
product characterizations to no. of sales
Increasing engagement by observing customer
behavior and mapping customer-item pairs to
interest indicators
Serve customer more efficienctly
Predicting demand. Observe high variability for
services/products
Automating tasks such as scoring credit
applications and insurance claims
Making enterprise apps predictive in prioritize
things, use adaptive workflows (route customer
support requests to best available person),
adapt the interface, set configurations and
preferences automatically
Future Applications
Medical imaging
simple chip utilizing cloud computing and deep
learning models
Baidu Deep Speechtranscribe voice queries in Mandarin
Practical Applications
Shopping Recommendation
better sales automation, lead generation,
efficient marketing, predictive hiring,
algorithmic trading
Churn Prediction
Face tagging
Sentiment Analysis
Understand emotions regardless of language
written
Youtube uses a deep neural network model to
generate higher-quality thumbnails for videos
Skype real-time translation
Video monitoring to identify interesting objects
such as dogs and trucks
Google Translate, Voice, Photos. Google
driverless car predict appropriate driving
actions
Numenta's Grok predict future energy
requirements and prices
LinkedIn predicts who you want to connect
with
Keywords
Machine only understand numbers. It starts
with obvious things and extends to subtle
things
Feature engineering
Finding connections between variables and
packing them into a new discreet variable
The Power to Predict Who Will Click, Buy, Lie, or
Die. [Predictive Analytics is) technology that
learns from experience [i.e. data] to predict the
future behavior of individuals in order to drive
better decisions
Pairing human workers with machine learning
and automation will transform knowledge work
and unleash new levels of human productivity
and creativity
Learning Paradigms
Learning ResourcesDeeplearning
first commercial-grade, open-source,
distributed deep-learning library written for
Java and Scala
Integrated with Hadoop and Spark, Deeplearning4j is designed for
business environments and includes a distributed multithreaded
deep-learning framework and a single-threaded deep-learning
framework
Definition
computers learning to predict from data
learning implies improvement through
gaining experience or knowledge
A (machine learning) computer program is said
to learn from experience E with respect to some
class of tasks T and performance measure P, if
its performance at tasks in T, as measured by P,
improves with experience E. (Tom Mitchell)
Applications
Natural Langauge Processing deep LSTM (long short-term memory)
Syntactic Pattern Recognition
Search Engines
from lexical matching (matching terms) to latent semantic
analysis (semantic matching) to deep neural network to extract
high-level semantic representations
Medical Diagonsis
Detection Credit Card fraud
Stock Market Analysis
Classifying DNA Sequence
Speech & handwriting sequences
Object Recognition in Computer Vision
Game Playing
Robot Locomotion
Multimedia Signal Processing
Image
Speech and Audio
Why M.L ? because we need to make machines ....
think like humans
notice similarties betwen things and generate new ideas
learn from mistakes
give explanation why things went wrong
Solve problems difficult or impossible for human to solve problems
Phenomena are changing rapidly
Application need to be customized for each user separately
No human experts
experts unable to explain thier experience
SAP HANA
an in-memory platform that runs analytics
applications smarter, business processes faster,
and data infrastructures simpler
Predictive Analysis Library (PAL)
Association
Classification
To predict a binary answer – i.e. Is this
transaction fraudulent or not?
Regression
To predict or score an amount that is a
non-binary value - i.e. Determining the
insurance risk factor this this driver
Cluster
To find groups in your dataset – i.e. Who are all
the people likely to buy my product today?
To predict future values based on previously
observed values – How likely are flight
cancellations in winter vs. summer months?
Time Series
Probability
Outlier
Automated Predictive Library (APL)
customers, developers, and partners do not
need to be data scientists to use the SAP APL –
they simply need to feed the APL what they
have and tell it what they need.
Classification, Regression, Clustering, TIme
Series, Key Influencers
SAP Lumira
an agile data discovery tool designed to
expedite data preparation and enable data to
be presented in a visual, easily digestible form
Deep Learning
Definition
Deep Learning is an algorithm which has no
theoretical limitations of what it can learn; the
more data you give and the more
computational time you provide, the better it is.
(Geoffrey Hinton)
Domains
Image
Faces
Machine vision
Sound
Speech to text
Machine translation
Text
Search
Information retrieval
Time Series
Weather
Biodata
Stocks
Why
CIO looking for highest performance in ML
Advance the state of the art in pattern
recognition and natural language processing
attempts to model high-level abstractions in
data
Differences
"Normal" neural networks usually have one to
two hidden layers and are used for SUPERVISED
prediction or classification.
SVMs are typically used for binary classification,
but occasionally for other SUPERVISED learning
tasks.
Deep learning neural network architectures
differ from "normal" neural networks because
they have more hidden layers. Deep learning
networks differ from "normal" neural networks
and SVMs because they can be trained in an
UNSUPERVISED or SUPERVISED manner for
both UNSUPERVISED and SUPERVISED learning
tasks
Data Scientist seek to process huge amount of
unstructured data
Algorithms
Deep Boltzman Machine (DBM)
Deep Belief Network (DBN)
Convolutional Neural Networks
Stacked Auto Encoders
Hierarachical Temporal Memory](https://image.slidesharecdn.com/machinelearning-170216092207/85/Machine-learning-Mindmap-1-320.jpg)
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Machine learning Mindmap
- 1. Machine Learning Enterprise Security/Fraud BrightPoint Sentinel automate threat detection and risk analysis HR/Recruiting Textio analyzed job text and outcomes data using listings from tens of thousands of companies hiQ People Analytics helps employee selection, development and retention by modeling historical data to predict future outcomes Sales Sentient Aware uses visual search to help shoppers quickly find the products they want to buy just like a store associate, connecting the right products to every customer Marketing LiftIgniter improves CTR, engagement and conversion by providing personalization using recommendation in real-time Customer Support Clarabridge collects customer feedback from various sources and provide actionable insights Quantifind tells what's most important in driving people to buy your products by introducing brand strategy. Explanatory analytics potential replacement for survey-based consumer research, brand health studies, focus groups, strategic consulting engagements, etc. Internal Intel Using the combination of machine learning and crowdsourcing from experts identified by their usage of tables, Alation centralizes the knowledge on data and ensures it’s always up-to-date Market Intel Mattermark mines and crunches public Internet data to provide investors, sales teams and others with search tools and other business intelligence, Purpose Increase Sales Performance 1) Don’t worry if data isn’t 100% accurate to begin with. As long as it’s directionally correct it will stimulate the right discussions. Data quality will improve naturally with use, feedback, updating, and iterative cleansing. 2) Drive excitement and adoption by making the application simple and engaging for the field, with easy-to-understand, interactive visualizations. 3) Integrate predictive analytics into the visualization and discovery process on a self-service basis so that new insights are intuitively delivered as the underlying data and attributes change. This will keep the insights from the application relevant. 4) Use iterative techniques to design and deliver a working app quickly and then adapt it based on user feedback. 5) Partner with IT through this process so that the users receive the desired self-service and flexibility while leveraging the business intelligence platform to maintain data governance, security, and control Increase number of customers reducing attrition/churn using historical data and look for likelihood of churn acquiring new customers by lead scoring and optimizing marketing campaigns Serve customer better cross-selling products optimizing products and pricing by mapping product characterizations to no. of sales Increasing engagement by observing customer behavior and mapping customer-item pairs to interest indicators Serve customer more efficienctly Predicting demand. Observe high variability for services/products Automating tasks such as scoring credit applications and insurance claims Making enterprise apps predictive in prioritize things, use adaptive workflows (route customer support requests to best available person), adapt the interface, set configurations and preferences automatically Future Applications Medical imaging simple chip utilizing cloud computing and deep learning models Baidu Deep Speechtranscribe voice queries in Mandarin Practical Applications Shopping Recommendation better sales automation, lead generation, efficient marketing, predictive hiring, algorithmic trading Churn Prediction Face tagging Sentiment Analysis Understand emotions regardless of language written Youtube uses a deep neural network model to generate higher-quality thumbnails for videos Skype real-time translation Video monitoring to identify interesting objects such as dogs and trucks Google Translate, Voice, Photos. Google driverless car predict appropriate driving actions Numenta's Grok predict future energy requirements and prices LinkedIn predicts who you want to connect with Keywords Machine only understand numbers. It starts with obvious things and extends to subtle things Feature engineering Finding connections between variables and packing them into a new discreet variable The Power to Predict Who Will Click, Buy, Lie, or Die. [Predictive Analytics is) technology that learns from experience [i.e. data] to predict the future behavior of individuals in order to drive better decisions Pairing human workers with machine learning and automation will transform knowledge work and unleash new levels of human productivity and creativity Learning Paradigms Learning ResourcesDeeplearning first commercial-grade, open-source, distributed deep-learning library written for Java and Scala Integrated with Hadoop and Spark, Deeplearning4j is designed for business environments and includes a distributed multithreaded deep-learning framework and a single-threaded deep-learning framework Definition computers learning to predict from data learning implies improvement through gaining experience or knowledge A (machine learning) computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. (Tom Mitchell) Applications Natural Langauge Processing deep LSTM (long short-term memory) Syntactic Pattern Recognition Search Engines from lexical matching (matching terms) to latent semantic analysis (semantic matching) to deep neural network to extract high-level semantic representations Medical Diagonsis Detection Credit Card fraud Stock Market Analysis Classifying DNA Sequence Speech & handwriting sequences Object Recognition in Computer Vision Game Playing Robot Locomotion Multimedia Signal Processing Image Speech and Audio Why M.L ? because we need to make machines .... think like humans notice similarties betwen things and generate new ideas learn from mistakes give explanation why things went wrong Solve problems difficult or impossible for human to solve problems Phenomena are changing rapidly Application need to be customized for each user separately No human experts experts unable to explain thier experience SAP HANA an in-memory platform that runs analytics applications smarter, business processes faster, and data infrastructures simpler Predictive Analysis Library (PAL) Association Classification To predict a binary answer – i.e. Is this transaction fraudulent or not? Regression To predict or score an amount that is a non-binary value - i.e. Determining the insurance risk factor this this driver Cluster To find groups in your dataset – i.e. Who are all the people likely to buy my product today? To predict future values based on previously observed values – How likely are flight cancellations in winter vs. summer months? Time Series Probability Outlier Automated Predictive Library (APL) customers, developers, and partners do not need to be data scientists to use the SAP APL – they simply need to feed the APL what they have and tell it what they need. Classification, Regression, Clustering, TIme Series, Key Influencers SAP Lumira an agile data discovery tool designed to expedite data preparation and enable data to be presented in a visual, easily digestible form Deep Learning Definition Deep Learning is an algorithm which has no theoretical limitations of what it can learn; the more data you give and the more computational time you provide, the better it is. (Geoffrey Hinton) Domains Image Faces Machine vision Sound Speech to text Machine translation Text Search Information retrieval Time Series Weather Biodata Stocks Why CIO looking for highest performance in ML Advance the state of the art in pattern recognition and natural language processing attempts to model high-level abstractions in data Differences "Normal" neural networks usually have one to two hidden layers and are used for SUPERVISED prediction or classification. SVMs are typically used for binary classification, but occasionally for other SUPERVISED learning tasks. Deep learning neural network architectures differ from "normal" neural networks because they have more hidden layers. Deep learning networks differ from "normal" neural networks and SVMs because they can be trained in an UNSUPERVISED or SUPERVISED manner for both UNSUPERVISED and SUPERVISED learning tasks Data Scientist seek to process huge amount of unstructured data Algorithms Deep Boltzman Machine (DBM) Deep Belief Network (DBN) Convolutional Neural Networks Stacked Auto Encoders Hierarachical Temporal Memory